- Apply quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how Meta users interact with consumer and business products.
- Mine massive amounts of data and perform large-scale data analysis to extract useful business insights.
- Develop data pipelines with automated, machine-learning systems that convert noisy core datasets into powerful signals of user behavior.
- Build models of user behaviors for analysis or to power production systems.
- Partner with Product and Engineering teams to solve problems and identify trends and opportunities.
- Design and implement dashboards and reports that track key business metrics and provide actionable insights.
- Inform, influence, support, and execute our product decisions and product launches by effectively communicating results to cross functional groups.
- Work across areas of product operations, exploratory analysis, product leadership, and data infrastructure to help shape the future of what we build at Meta.
- Requires a Bachelor's degree (or foreign degree equivalent) in Computer Science, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field and 2 years of experience in the job offered or a computer-related field.
- Experience must include 2 years of experience in the following:
- 1. Quantitative analysis techniques: clustering, regression, pattern, recognition, or descriptive and inferential statistics
- 2. Data analysis and algorithm development
- 3. Optimization, probability, statistics, or machine learning
- 4. ETL (Extract, Transform, Load) processes
- 5. Relational databases and SQL
- 6. Large scale data processing infrastructure using distributed systems ( Hive or Teradata)
- 7. Experience with data analysis and statistical modeling using the R or Python ecosystems, with packages such as pandas, statsmodels, scikit-learn, tidyverse (dplyr, ggplot2, etc.),R,MATLAB or SAS
- 8. Technical presentation skills
- 9. Working with cross-functional teams to choose/create metrics and set metric goals
- 10. Designing and executing experiments (e.g. A/B tests, country tests, network tests), and
- 11. Technical presentation skills to influence technical and non-technical audiences (engineers, data scientists, program managers, marketers
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